EC-Net: an Edge-aware Point set Consolidation Network
文献类型：会议论文
| 作者 | Lequan Yu; Xianzhi Li; Chi-Wing Fu; Daniel Cohen-Or; Pheng-Ann Heng |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | 德国，慕尼黑 |
| 英文摘要 | Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required to be consolidated. In this paper, we present the rst deep learning based edge-aware technique to facilitate the consolidation of point clouds. We design our network to process points grouped in local patches, and train it to learn and help consolidate points, deliberately for edges. To achieve this, we formulate a regression component to simultaneously recover 3D point coordinates and pointto- edge distances from upsampled features, and an edge-aware joint loss function to directly minimize distances from output points to 3D meshes and to edges. Compared with previous neural network based works, our consolidation is edge-aware. During the synthesis, our network can attend to the detected sharp edges and enable more accurate 3D reconstructions. Also, we trained our network on virtual scanned point clouds, demonstrated the performance of our method on both synthetic and real point clouds, presented various surface reconstruction results, and showed how our method outperforms the state-of-the-arts. |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/13772]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 推荐引用方式 GB/T 7714 | Lequan Yu,Xianzhi Li,Chi-Wing Fu,et al. EC-Net: an Edge-aware Point set Consolidation Network[C]. 见:. 德国，慕尼黑. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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